Machine Learning Engineer Radar Jobs

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Principal Product Architect Mastercard powers economies and empowers people in more than 200 countries and territories worldwide. Together with our customers, we are building a sustainable economy where everyone can prosper. Through secure, simple and accessible digital payments, our technology, partnerships and networks help people, businesses and governments reach their full potential. A2A Risk Solutions: Principal Product Architect We are seeking a Principal Product Architect to join our award-winning Account-to-Account (A2A) Risk Solutions team. Our products combine advanced data science and payments expertise to help financial institutions combat fraud and financial crime. This role will be central to designing and scaling Mastercard's global A2A Risk Solutions. Working closely with product, engineering, data science, customers, financial institutions and technology partners, you will define scalable architecture patterns, shape technology strategy and support expansion into new markets. Responsibilities Define and maintain solution architectures that align with business objectives, product requirements and architectural principles. Translate business, customer and market requirements into scalable, secure and resilient technical designs. Lead architecture for A2A fraud and f

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location 1 North Coast Drive, Singapore Department Product Integration Engineering, Yield Enhancement Project Title Data Analytics for Yield Enhancement in Advanced Memory Manufacturing Project Description This internship provides structured exposure to yield enhancement challenges in advanced memory manufacturing. Through guided learning and supervised technical exploration, the intern will gain practical experience applying data analytics techniques to understand yield behaviour and identify potential improvement opportunities. The project will involve semiconductor probe-test data, electrical failure modes, fabrication-process metrics, multivariate analysis, Python-based analytics, and introductory Machine Learning techniques. The intern will also explore approved Artificial Intelligence and Generative AI tools to enhance data exploration, solution development, and technical documentation. The project emphasizes learning, skills development, and the practical application of analytical methods in a manufacturing environment. It is designed to prepare the intern for future engineering and data-analysis roles in the semiconductor industry. Objective of the Project Develop an understanding of yield b

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. The DASG Architecture Lab and Customer Enablement Lab (CEL) teams provide the infrastructure, platforms, and technical expertise required to accelerate technology pathfinding, AI and system workload analysis, memory subsystem characterization, FPGA/LPDRAM prototyping, and customer enablement initiatives. These teams play a critical role in enabling architecture exploration, validation, and teamwork across engineering organizations and external partners. AI Efficiency and Usage is a must. AI proficiency or AI‑enabled ways of working where applicable. Agentic AI and Generative AI usage model is a must. Responsibilities Own the day-to-day bring-up, readiness, maintenance, and operational support of lab systems, racks, benches, test equipment, and reference platforms used by DASG Architecture Lab and Customer Enablement Lab teams. Deploy, configure, and maintain server, GPU, FPGA, client AI, mobile AI, analyzer, and environmental test equipment supporting workload characterization and technology pathfinding activities. Build and maintain stable, reproducible validation environments, including BIOS, BMC, firmware, operating systems, drivers, networking, remote access, and power and thermal configurations. Collaborate with architecture and validation teams to enable memory trace capture, workload analysis, DRAM replay, power and performance characterization, and data-driven engineering decisions. Develop and maintain operational processes for asset tracking, configuration management, equipme

About the Team: The Workforce Demand Forecasting team is part of Global Support and Integrity Operations. We help DoorDash deliver reliable, efficient, and high quality support by translating business activity into demand forecasts and actionable planning insights. Our work informs customer experience, workforce investment, operational readiness, and cost. We partner with Workforce Management, Operations, Finance, Business Intelligence, Data Science, Product, and Engineering to improve forecast accuracy, strengthen planning decisions, and build scalable operating mechanisms. The team supports a broad set of global support workstreams. Our goal is to provide leaders with the forecasts, insights, and decision frameworks needed to balance service, customer experience, and cost. About the Role: We are seeking an Associate Manager to lead a defined set of Forecasting workstreams and strengthen the team’s operating model. This role goes beyond forecast production. You will own workstream performance from end to end, including forecast quality, prioritization, business readiness, stakeholder alignment, and continuous improvement. You will translate complex data into clear recommendations, make informed tradeoffs, and help leaders understand key risks, opportunities, and decisions. You will also guide and develop analysts through coaching, technical support, and clear performance expectations. The ideal candidate combines analytical depth, strategic judgment, operational discipline, and strong cross functional influence. Success in this role requires advanced SQL skills, strong business acumen, and demonstrated experience improving forecasting, planning, or operational processes at scale. You’re excited about this opportunity because you will own: Forecasting Performance and Business Readiness Lead demand forecasting across designated business lines and workstreams, with accountability for forecast accuracy, assumptions, risks, and recommended actions across planning horizo

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. Role Overview As the Okta Research & Design (ORD) Jira Owner & Administrator , nested within the Technical Program Management (TPM) organization, you will define and execute the strategy for how ORD plans, tracks, and reports on engineering work within the full toolchain of Atlassian Jira, Jira Advanced Roadmaps (Plans), Confluence, Rovo, and AI integrations. In this role, you act as a critical bridge between product & technical leadership, agile teams, and cross-functional business units — translating methodology, best practices, and process frameworks into practical workflow improvements that accelerate delivery and reduce manual overhead for a 1,000+ person engineering, product, and design organization. By optimizing tooling configurations, automating reporting, and eliminating fragmented management processes, you will directly accelerate ORD’s developer velocity and strengthen operational efficiency. Core Responsibilities 1. Platform Ownership & Strategic Tooling Direction Strategic Vision: Define, implement, govern an AI-led strategy for utilizing Jira for product planning, tracking, and reporting tooling that supports end-to-end portfolio tracking from high-level corporate objectives down to sprint-level stories. E2E Ecosystem Management: Complete ORD ownership of the toolchain (Jira, Jira advanced Roadmaps, AI integrations, Confluence), overseeing architecture, schema updates, workflow management, screen/notification schemes, cu

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

About the Role The Agents team investigates how to build, align, and scale frontier AI systems that can tackle complex, multi-step tasks and workflows across text and speech, with a particular focus on agentic and scientific domains. Our work sits at the intersection of agent capabilities, human-computer interaction, and infrastructure—from designing post-training methods for agentic behavior to developing evaluation frameworks for open-ended tasks where traditional metrics fall short. As a research intern, you will work on problems at the frontier of agentic AI, where challenges in alignment, reliability, and scalability are deeply intertwined. Projects may involve developing new training recipes for self-learning and long-horizon reasoning, curating datasets for non-deterministic scientific and agentic tasks, studying failure modes in agentic behavior, or building infrastructure that enables agent operations at scale. You'll operate in a space where algorithmic innovation, dataset and interaction design, and systems work come together to push the boundaries of what AI agents can reliably accomplish. Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Communicate the plans, progress, and results of projects to the broader team Requirements Currently pursuing a Ph.D. degree in Computer Science, Electrical Engineering, Information Science, or a related field Publications at leading ML, NLP, or speech conferences or journals (such as NeurIPS, ICML, ICLR, *ACL, EMNLP, Interspeech) Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Understanding of how LLMs work Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Example Research Directions Training, develo

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. About the Role Hybrid (3 days per week in the London office required) Individual contributor role Fraud is constantly evolving. As our business grows, so do the ways bad actors seek to exploit our products, promotions, and operations. As the Senior Strategy & Operations Manager for Emerging Fraud & Abuse, you'll lead the discovery, investigation, and mitigation of new and evolving abuse vectors across our platform. Your mission is to identify risks before they become systemic, working across Product, Engineering, Data Science, Operations, Legal, and Policy to design scalable solutions that protect our customers, partners, and business. This isn't a role focused on running existing fraud programmes. It's about identifying what's next. You'll combine deep analytical thinking with strategic problem solving to uncover emerging threats, quantify business impact, influence product strategy, and build new controls that keep us ahead of increasingly sophisticated fraudsters. If you enjoy solving ambiguous problems, following the data, and building solutions where no playbook exists, we'd love to hear from you. What You'll Do Identify Emerging Abuse Proactively identify new fraud patterns, abuse trends, and vulnerabilities across our products, customer journeys, and operational processes. Investigate anomalies, understand attacker behaviours, and translate observations into actionable mitigation strategies. Continuously scan for evolving risks, whether driven by organised fraud, policy loopholes, technological change, or new product launches. Build the Strategy Define the roadmap for emerging fraud prevention across the business. Prioritise risks based on

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location Micron Singapore, Fab 10 (1 North Coast Drive, Singapore 757432) Department Photolithography Process and Equipment Engineering Project Title AI-Enabled Quality Drift Detection and Engineering Workflow Automation Project Description Advanced data analytics and machine learning are increasingly used in semiconductor manufacturing to improve efficiency, process control, and quality performance. This internship project focuses on applying data analytics and machine learning techniques to large-scale quality datasets for faster detection of quality-metrics drift. The intern will contribute to an end-to-end solution covering data querying, preprocessing, analytics, modelling, and results visualization. Objective of the Project Develop analytical models for monitoring key quality metrics and detecting potential drift. Apply machine learning and multivariable analysis to large-scale test and measurement data. Create automated workflows that improve engineering productivity and analytical efficiency. Develop visualizations that communicate model results and quality trends effectively. Opportunities for Full Tim

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location F10A Singapore, 1 North Coast Avenue Singapore 757432 Department High Bandwidth Memory Product and Systems Engineering, Industrial Engineering Management Project Title AI-Enabled High Bandwidth Memory Sample Planning and Workflow Optimization Project Description As an intern in the High Bandwidth Memory Product and Systems Engineering Industrial Engineering team, you will participate in a structured project to improve the visibility and planning of High Bandwidth Memory sample workflows through data analytics, dashboard development, and Artificial Intelligence. The project focuses on mapping the end-to-end sample workflow from Die Bank through probe, test, shipment, and the Reliability Lab. The intern will develop an AI-Enabled solution to predict sample arrival and reliability start dates, forecast burn-in resource demand, automate selected notifications, and provide operational insights through interactive dashboards. The internship provides hands-on exposure to semiconductor sample logistics, process documentation, predictive analytics, business intelligence, and workflow optimization. The intern will collaborate with engineering, planning, operations, and information techn

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location Micron Singapore Bendemeer (990 Bendemeer Road) Department Test Engineering, Electrical Failure Analysis and Physical Failure Analysis Project Title AI-Driven Semiconductor Failure Analysis and Laboratory Digitalisation Project Description This internship provides an opportunity to gain hands-on exposure to semiconductor failure analysis methodologies, laboratory automation, and AI-Enabled engineering workflows. The project focuses on leveraging data analytics, digital tools, and Artificial Intelligence to enhance failure analysis efficiency, improve knowledge management, and accelerate engineering decision-making. The intern will learn how Electrical Failure Analysis and Physical Failure Analysis techniques are used to identify the root causes of product and process issues across memory technologies in a high-volume manufacturing environment. Objective of the Project Develop a digital or AI-Enabled solution that improves failure analysis workflow efficiency. Apply data analytics and visualization tools to enhance laboratory process visibility. Explore automation opportunities that improve engineering productivity and knowledge accessibility. </

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,500 firms in 60 countries use Addepar to manage and advise on nearly $10 trillion in assets. Its open platform integrates with 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and levels of complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, São Paulo, London, Edinburgh, Geneva, Warsaw, Dubai, Pune and Singapore. The Role Addepar has just launched an Alternatives Product offering that gives Addepar clients the option to have their Private Funds data extracted, processed, validated, and ingested directly from Private Fund documents for a more automated end-to-end solution for handling Alternatives on the platform. This white-glove solution combines proprietary machine learning technology that automatically extracts data from Private Funds documents and human-in-loop review to validate any documents that fail to pass confidence and data quality checks. As part of the broader Addepar Data organization, the Alternatives Data Ops team is responsible for the human-in-loop review of Private Funds documents, ensuring clients' Alternatives data is processed, validated, and available in Addepar in a timely manner. This role will include managing a team and reviewing Private Fund statements, modifying data that has been incorrectly extracted by our machine learning models, and reprocessing the data within SLAs. The Alternatives Data Ops team plays a critical role in the success of Addepar's Alternatives offering and requires daily communications with the Alternatives Product Team, Machine Learning Operations, clients, data providers (GPs and Fund Admins), and addi

About Sapience AI Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This is a research role focused on the models at the foundation of collective intelligence. You study, adapt, and advance the foundational models that power how Sapience AI understands language, knowledge, and reasoning. You work where research meets the platform: designing experiments, evaluating models, adapting them to the demands of professional communities, and feeding what you learn into the COGENT architecture and MINERVA. You bring scientific rigor to a fast-moving field, and you turn that rigor into advances the product can actually use. Why this role exists The quality of collective intelligence depends on the models beneath it. How well the platform understands a community’s language, grounds its answers, and reasons over knowledge starts with foundational model work done well. The field moves quickly, and not every advance is real or ready. Someone has to separate genuine progress from noise and turn the real advances into something the platform can rely on. The Foundational Model Research Data Scientist does that. You run the experiments, evaluate honestly, and translate frontier progress into dependable capability for Sapience AI. What y